• Lead ML engineering execution • Own technical direction for ML systems across computer vision and multimodal/GenAI use cases. • Design and develop advanced algorithms to address complex business challenges. • Research and implement cutting-edge machine learning models and techniques. • Prototype new models and evaluate their performance through simulation. • Optimize machine learning models to achieve faster speed and greater efficiency. • Implement feature selection and engineering techniques to boost model performance. • Evaluate machine learning models to ensure they deliver accurate and relevant results. • Set clear success criteria for models and systems, then hold the team accountable to outcomes. • Build and scale the team • Anticipate skill gaps and build a deliberate hiring plan; consistently attract and retain strong ML engineers. • Coach with depth: set explicit expectations, give direct feedback, and accelerate individual growth. • Build an environment of ownership, trust, and continuous learning. • Develop future leaders and create succession within the team. • Partner cross-functionally • Work closely with Product, Hardware, Data Science, and Analytics to shape problem definition and roadmap. • Translate ambiguous business needs into concrete technical direction. • Contribute to company strategy at the intersection of ML, devices, and care outcomes.